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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Large Language Model-Powered Diagnostic Co-Pilot ("CapyEngine") for Mental Disorders: Development, Evaluation, and

Liying Wang1,2, Yunzhang Jiang3

  • 1Institute on Digital Health and Innovation, College of Nursing , Florida State University, 222 S Copeland St, Tallahassee, FL, 32306, United States, 1 (850) 644-3296.

JMIR AI
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Summary
This summary is machine-generated.

This study developed CapyEngine, an AI diagnostic tool for mental health. While ChatGPT-4o showed higher accuracy, CapyEngine demonstrated more consistent diagnostic rankings, suggesting potential for clinical augmentation.

Keywords:
ChatGPT-4LLMaccuracy ratediagnosislarge language modelmental disorders

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Mental Health Diagnostics
  • Clinical Decision Support Systems

Background:

  • Limited evidence exists on large language models' (LLMs) diagnostic capabilities in mental health.
  • The study addresses the need for AI tools to assist in mental disorder diagnosis.

Purpose of the Study:

  • To develop and evaluate CapyEngine, an LLM-powered tool for mental disorder diagnosis.
  • To compare CapyEngine's diagnostic accuracy against ChatGPT-4o and clinicians.

Main Methods:

  • CapyEngine was developed using LLMs, embedding models, and vector searches, with a database from DSM-5-TR.
  • Usability testing was conducted with mental health professionals.
  • Diagnostic accuracy was compared using standardized case scenarios against ChatGPT-4o and clinicians.

Main Results:

  • ChatGPT-4o outperformed CapyEngine and clinicians in broader diagnostic rankings (top 10 and top 5).
  • Clinicians showed higher accuracy than CapyEngine for the top 5 benchmark.
  • CapyEngine demonstrated the most consistent diagnostic ranking behavior across stringent benchmarks.

Conclusions:

  • ChatGPT-4o achieved higher accuracy at less stringent benchmarks.
  • CapyEngine's domain-specific design resulted in consistent rankings, showing promise for augmenting mental health diagnostics.
  • Further research is needed to evaluate AI integration into clinical workflows.